SafetyOnline
AdvBench
Measures whether adversarial suffixes induce aligned language models to produce targeted harmful behavior.
- Task count
- 520
There are 25 safety in the index.
25 benchmarks
SafetyOnline
Measures whether adversarial suffixes induce aligned language models to produce targeted harmful behavior.
SafetyOnline
Evaluates agent task utility and resistance to prompt injection while agents use tools over untrusted data.
SafetyOnline
Measures social bias in question answering across ambiguous and disambiguated contexts covering nine protected dimensions.
SafetyOnline
Provides a compact BIG-bench version of BBQ for exact-match evaluation of bias-sensitive question answering.
SafetyOnline
Supports safety classification and preference evaluation with human-labeled question-answer pairs across harm categories.
SafetyOnline
Evaluates model and defense robustness against indirect prompt injection across five application tasks.
SafetyOnline
Measures social biases in open-ended English generation from prompts spanning profession, gender, race, religion, and politics.
SafetyOnline
Measures stereotypical preferences in masked language models using paired sentences across nine types of social bias.
SafetyOnline
Evaluates cybersecurity risks from model-generated code and responses to requests that could enable cyberattacks.
SafetyOnline
Evaluates automated red-teaming attacks and model refusal defenses across standardized harmful behaviors.
SafetyOnline
Evaluates whether tool-using agents follow indirect prompt injections embedded in external tool responses.
SafetyOnline
Compares jailbreak attacks and defenses on a standardized set of misuse behaviors and evaluation rules.
SafetyOnline
Measures whether language models continue naturally occurring web prompts with toxic text.
SafetyOnline
Evaluates autonomous-driving agents in safety-critical simulated scenarios, including adversarially generated cases.
SafetyOnline
Evaluates large-language-model safety knowledge with Chinese and English multiple-choice questions across seven risk categories.
SafetyOnline
Measures the safety-refusal behavior of language models across a balanced taxonomy of unsafe requests and prompt mutations.
SafetyOnline
Measures stereotypical bias and language-modeling ability in sentence-level and discourse-level contexts.
SafetyOnline
Evaluates jailbreak responses with a forbidden-prompt dataset and an automated judge designed to penalize empty refusals and ineffective attacks.
SafetyOnline
Tests hate-speech classifiers on machine-generated implicit toxic and benign statements about 13 minority groups.
SafetyOnline
Measures gender bias in coreference resolution by changing pronoun gender while preserving sentence structure.
SafetyOnline
Tests whether safety-tuned language models refuse harmless prompts that superficially resemble unsafe requests.
SafetyOnline
Tests resistance to malicious tool descriptions, tool shadowing, and excess privilege.
SafetyOnline
Tests whether a model resists endorsing a user's increasingly harmful mistaken plan.
SafetyOnline
Evaluates whether language models decline risky instructions while still answering harmless requests.
Published by AI Benchmark Index. How we check each benchmark.